Hierarchical Logistic Regression failed (Runtime error 139)

Interfaces
1

Hello all. I’m just getting started with Stan in python via cmdstanpy. I’m using the associated conda environment, and using the stock regression model in the stan manual (see Regression Models) to get something working before I get more adventurous. I have some replicated dose response experiments, and I’m interested in describing the variation on that level (‘ll’ in the data). I don’t have a full set of doses for some of the reps, but I’m hoping to borrow some information from the more complete reps.

I get the retcode -11 error, which I take to be 139. I’m outputting files to my working directory, and they are empty stdout.txt files. I’m wondering how I should go about starting to troubleshoot this. My data json looks like this:

{"D": 1, "N": 1946, "L": 3, "y": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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Here is the model for convenience:

data {
  int<lower=1> D;     // number of predictors
  int<lower=0> N;     // number of observations
  int<lower=1> L;     // number of replicates
  array[N] int<lower=0, upper=1> y;
  array[N] int<lower=1, upper=L> ll;     // replicate designation (1 through L)
  array[N] row_vector[D] x;     // predictors (dose)
}
parameters {
  array[D] real mu;     // mean
  array[D] real<lower=0> sigma;   // standard dev, positive restriction
  array[L] vector[D] beta; 
}
model {
  for (d in 1:D) {
    mu[d] ~ normal(0, 100);
    for (l in 1:L) {
      beta[l, d] ~ normal(mu[d], sigma[d]);
    }
  }
  for (n in 1:N) {
    y[n] ~ bernoulli(inv_logit(x[n] * beta[ll[n]]));
  }
}

And here is the compiling, fitting, etc.:

my_stanfile = os.path.join('.', 'logistic.stan')
my_model = stan.CmdStanModel(stan_file=my_stanfile, cpp_options={'STAN_THREADS':'true'})
my_model.name
my_model.stan_file
my_model.exe_file
my_model.code()

my_model.sample(data='data.json', show_console=True, iter_sampling=2000, 
                chains=1, output_dir=os.path.join('.', 'runfiles'), inits=0)

Does anything jump out as super illegal here? Any basic troubleshooting steps would be great since I’m not getting a lot of info to stdout.txt (I guess the error is happening pretty immediately).

Thanks!

2

When I try running your model, I get an error loading the data:

ll[1] is 0, but must be greater than or equal to 1.000000 (in 'main.stan', line 6, column 2 to column 36)

It sounds like you have another issue though, because you should be seeing this message rather than just a generic error code.

Passing show_console=True to sample may help, but if the program is dying at startup it is likely no output is being produced.

You can try running the model directly from the command line, which might provide some more insight as well.

Which platform/operating system are you using?

3

Thanks for taking a look.

And good catch - I need to change my replicate data to 1:4 from 0:3 (data ‘ll’).

When I run via command line I get a segmentation fault (core dumped).

Ubuntu 24.04.2.

My conda environment is build from the cmdstanpy list on conda-forge. I’ve installed a few more python libraries - hopefully none that messed with the cmdstan install.

# packages in environment at /home/daniel-hartman/anaconda3/envs/stan:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       2_gnu    conda-forge
asttokens                 3.0.0           py313h06a4308_0  
binutils_impl_linux-64    2.43                 h4bf12b8_4    conda-forge
binutils_linux-64         2.43                 h4852527_4    conda-forge
brotli-python             1.0.9           py313h6a678d5_9  
bzip2                     1.0.8                h4bc722e_7    conda-forge
c-ares                    1.19.1               h5eee18b_0  
ca-certificates           2025.4.26            hbd8a1cb_0    conda-forge
cmdstan                   2.36.0               h6b7b5dd_0    conda-forge
cmdstanpy                 1.2.5              pyhd8ed1ab_0    conda-forge
colorama                  0.4.6              pyhd8ed1ab_1    conda-forge
comm                      0.2.1           py313h06a4308_0  
contourpy                 1.3.1           py313hdb19cb5_0  
cycler                    0.11.0             pyhd3eb1b0_0  
cyrus-sasl                2.1.28               h52b45da_1  
debugpy                   1.8.11          py313h6a678d5_0  
decorator                 5.1.1              pyhd3eb1b0_0  
executing                 0.8.3              pyhd3eb1b0_0  
expat                     2.7.0                h5888daf_0    conda-forge
fontconfig                2.14.1               h55d465d_3  
fonttools                 4.55.3          py313h5eee18b_0  
freetype                  2.13.3               h4a9f257_0  
gcc_impl_linux-64         13.3.0               h1e990d8_2    conda-forge
gcc_linux-64              13.3.0              hc28eda2_10    conda-forge
gxx_impl_linux-64         13.3.0               hae580e1_2    conda-forge
gxx_linux-64              13.3.0              h6834431_10    conda-forge
icu                       73.1                 h6a678d5_0  
ipykernel                 6.29.5          py313h06a4308_1  
ipython                   9.1.0           py313h06a4308_0  
ipython_pygments_lexers   1.1.1           py313h06a4308_0  
ipywidgets                8.1.6              pyhd8ed1ab_0    conda-forge
jedi                      0.19.2          py313h06a4308_0  
jpeg                      9e                   h5eee18b_3  
jupyter_client            8.6.3           py313h06a4308_0  
jupyter_core              5.7.2           py313h06a4308_0  
jupyterlab_widgets        3.0.14             pyhd8ed1ab_0    conda-forge
kernel-headers_linux-64   3.10.0              he073ed8_18    conda-forge
kiwisolver                1.4.8           py313h6a678d5_0  
krb5                      1.20.1               h143b758_1  
lcms2                     2.16                 h92b89f2_1  
ld_impl_linux-64          2.43                 h712a8e2_4    conda-forge
lerc                      4.0.0                h6a678d5_0  
libabseil                 20250127.0      cxx17_h6a678d5_0  
libblas                   3.9.0           31_h59b9bed_openblas    conda-forge
libcblas                  3.9.0           31_he106b2a_openblas    conda-forge
libcups                   2.4.2                h2d74bed_1  
libcurl                   8.12.1               hc9e6f67_0  
libdeflate                1.22                 h5eee18b_0  
libedit                   3.1.20230828         h5eee18b_0  
libev                     4.33                 h7f8727e_1  
libexpat                  2.7.0                h5888daf_0    conda-forge
libffi                    3.4.6                h2dba641_1    conda-forge
libgcc                    14.2.0               h767d61c_2    conda-forge
libgcc-devel_linux-64     13.3.0             hc03c837_102    conda-forge
libgcc-ng                 14.2.0               h69a702a_2    conda-forge
libgfortran               14.2.0               h69a702a_2    conda-forge
libgfortran5              14.2.0               hf1ad2bd_2    conda-forge
libglib                   2.78.4               hdc74915_0  
libgomp                   14.2.0               h767d61c_2    conda-forge
libhwloc                  2.11.2          default_h0d58e46_1001    conda-forge
libiconv                  1.18                 h4ce23a2_1    conda-forge
liblapack                 3.9.0           31_h7ac8fdf_openblas    conda-forge
liblzma                   5.8.1                hb9d3cd8_0    conda-forge
libmpdec                  4.0.0                h4bc722e_0    conda-forge
libnghttp2                1.57.0               h2d74bed_0  
libopenblas               0.3.29          pthreads_h94d23a6_0    conda-forge
libpng                    1.6.39               h5eee18b_0  
libpq                     17.4                 hdbd6064_0  
libprotobuf               5.29.3               hc99497a_0  
libsanitizer              13.3.0               he8ea267_2    conda-forge
libsodium                 1.0.18               h7b6447c_0  
libsqlite                 3.46.0               hde9e2c9_0    conda-forge
libssh2                   1.11.1               h251f7ec_0  
libstdcxx                 14.2.0               h8f9b012_2    conda-forge
libstdcxx-devel_linux-64  13.3.0             hc03c837_102    conda-forge
libstdcxx-ng              11.2.0               h1234567_1  
libtiff                   4.7.0                hde9077f_0  
libuuid                   1.41.5               h5eee18b_0  
libwebp-base              1.3.2                h5eee18b_1  
libxcb                    1.15                 h7f8727e_0  
libxkbcommon              1.0.1                h097e994_2  
libxml2                   2.13.7               hfdd30dd_0  
libzlib                   1.2.13               h4ab18f5_6    conda-forge
lz4-c                     1.9.4                h6a678d5_1  
make                      4.4.1                hb9d3cd8_2    conda-forge
matplotlib                3.10.0          py313h06a4308_1  
matplotlib-base           3.10.0          py313h68cf311_1  
matplotlib-inline         0.1.6           py313h06a4308_0  
mysql                     8.4.0                h721767e_2  
ncurses                   6.5                  h2d0b736_3    conda-forge
nest-asyncio              1.6.0           py313h06a4308_0  
numpy                     2.2.5           py313h17eae1a_0    conda-forge
openjpeg                  2.5.2                h0d4d230_1  
openldap                  2.6.4                h42fbc30_0  
openssl                   3.5.0                h7b32b05_0    conda-forge
packaging                 24.2            py313h06a4308_0  
pandas                    2.2.3           py313ha87cce1_3    conda-forge
parso                     0.8.4           py313h06a4308_0  
pcre2                     10.42                hebb0a14_1  
pexpect                   4.8.0              pyhd3eb1b0_3  
pillow                    11.1.0          py313hac6e08b_1  
pip                       25.1               pyh145f28c_0    conda-forge
platformdirs              4.3.7           py313h06a4308_0  
prompt-toolkit            3.0.43          py313h06a4308_0  
prompt_toolkit            3.0.43               hd3eb1b0_0  
psutil                    5.9.0           py313h5eee18b_1  
ptyprocess                0.7.0              pyhd3eb1b0_2  
pure_eval                 0.2.2              pyhd3eb1b0_0  
pygments                  2.19.1          py313h06a4308_0  
pyparsing                 3.2.0           py313h06a4308_0  
pyqt                      6.7.1           py313h6a678d5_1  
pyqt6-sip                 13.9.1          py313h5eee18b_1  
python                    3.13.2          hf623796_100_cp313  
python-dateutil           2.9.0.post0        pyhff2d567_1    conda-forge
python-tzdata             2025.2             pyhd8ed1ab_0    conda-forge
python_abi                3.13                    7_cp313    conda-forge
pytz                      2025.2             pyhd8ed1ab_0    conda-forge
pyzmq                     26.2.0          py313h6a678d5_0  
qtbase                    6.7.3                hdaa5aa8_0  
qtdeclarative             6.7.3                h6a678d5_0  
qtsvg                     6.7.3                he621ea3_0  
qttools                   6.7.3                h80c7b02_0  
qtwebchannel              6.7.3                h6a678d5_0  
qtwebsockets              6.7.3                h6a678d5_0  
readline                  8.2                  h8c095d6_2    conda-forge
setuptools                75.8.0          py313h06a4308_0  
sip                       6.10.0          py313h6a678d5_0  
six                       1.17.0             pyhd8ed1ab_0    conda-forge
sqlite                    3.45.3               h5eee18b_0  
stack_data                0.2.0              pyhd3eb1b0_0  
stanio                    0.5.1              pyhd8ed1ab_1    conda-forge
sysroot_linux-64          2.17                h0157908_18    conda-forge
tbb                       2022.1.0             h4ce085d_0    conda-forge
tbb-devel                 2022.1.0             h1f99690_0    conda-forge
tk                        8.6.14               h39e8969_0  
tornado                   6.4.2           py313h5eee18b_0  
tqdm                      4.67.1             pyhd8ed1ab_1    conda-forge
traitlets                 5.14.3          py313h06a4308_0  
tzdata                    2025b                h78e105d_0    conda-forge
wcwidth                   0.2.5              pyhd3eb1b0_0  
widgetsnbextension        4.0.14             pyhd8ed1ab_0    conda-forge
xcb-util-cursor           0.1.4                h5eee18b_0  
xz                        5.6.4                h5eee18b_1  
zeromq                    4.3.5                h6a678d5_0  
zlib                      1.2.13               h4ab18f5_6    conda-forge
zstd                      1.5.6                hc292b87_0
4

Segfault isn’t good! Do you have gdb installed? If so can you try getting a back trace?

Otherwise, if you’re able to upload a core dump file I can try to look around

5

I do have gdb, but I can’t say I’m well versed in that. I can do gdb logistic but I guess I need some breakpoints and c++ is a little outside of my wheelhouse.

Similarly, I’m not getting much out of the coredump, but here we go!

6

You can do gdb logistic and then run sample chains.... It should automatically hit a break when the segfault happens, at which point you can see if the command bt gives anything interesting.

I also noticed your conda environment looks like it has some packages from conda-forge and some from the default channels – the conda-forge folks generally recommend against this, so you might want to try a second environment to see if that makes any difference?